SoroushVahidi/scidocs huggingface dataset:
On the Performance of Intel SGX
(2016)

As cloud computing is widely used in various fields, more and more individuals and organizations are considering outsourcing data to the public cloud. However, the security of the cloud data has become the most prominent concern of many customers, especially those who possess a large volume of valuable and sensitive data. Although some technologies like Homomorphic Encryption were proposed to solve the problem of secure data, the result is still not satisfying. With the advent of Intel SGX processor, which aims to thoroughly eliminate the security concern of cloud environment in a hardware-assisted approach, it brings us a number of questions on its features and its practicability for the current cloud platform. To evaluate the potential impact of Intel SGX, we analyzed the current SGX programming mode and inferred some possible factors that may arise the overhead. To verify our performance hypothesis, we conducted a systematic study on SGX performance by a series of benchmark experiments. After analyzing the experiment result, we performed a workload characterization to help programmer better exploit the current availability of Intel SGX and identify feasible research directions.
A Generative Modeling Approach to Limited Channel ECG Classification
(2018)

Processing temporal sequences is central to a variety of applications in health care, and in particular multichannel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling. While Recurrent Neural Networks (RNNs) have led to significant advances in automated diagnosis with time-series data, they perform poorly when models are trained using a limited set of channels. A crucial limitation of existing solutions is that they rely solely on discriminative models, which tend to generalize poorly in such scenarios. In order to combat this limitation, we develop a generative modeling approach to limited channel ECG classification. This approach first uses a Seq2Seq model to implicitly generate the missing channel information, and then uses the latent representation to perform the actual supervisory task. This decoupling enables the use of unsupervised data and also provides highly robust metric spaces for subsequent discriminative learning. Our experiments with the Physionet dataset clearly evidence the effectiveness of our approach over standard RNNs in disease prediction.
A Boosted Bayesian Multiresolution Classifier for Prostate Cancer Detection From Digitized Needle Biopsies
(2012)

Diagnosis of prostate cancer (CaP) currently involves examining tissue samples for CaP presence and extent via a microscope, a time-consuming and subjective process. With the advent of digital pathology, computer-aided algorithms can now be applied to disease detection on digitized glass slides. The size of these digitized histology images (hundreds of millions of pixels) presents a formidable challenge for any computerized image analysis program. In this paper, we present a boosted Bayesian multiresolution (BBMR) system to identify regions of CaP on digital biopsy slides. Such a system would serve as an important preceding step to a Gleason grading algorithm, where the objective would be to score the invasiveness and severity of the disease. In the first step, our algorithm decomposes the whole-slide image into an image pyramid comprising multiple resolution levels. Regions identified as cancer via a Bayesian classifier at lower resolution levels are subsequently examined in greater detail at higher resolution levels, thereby allowing for rapid and efficient analysis of large images. At each resolution level, ten image features are chosen from a pool of over 900 first-order statistical, second-order co-occurrence, and Gabor filter features using an AdaBoost ensemble method. The BBMR scheme, operating on 100 images obtained from 58 patients, yielded: 1) areas under the receiver operating characteristic curve (AUC) of 0.84, 0.83, and 0.76, respectively, at the lowest, intermediate, and highest resolution levels and 2) an eightfold savings in terms of computational time compared to running the algorithm directly at full (highest) resolution. The BBMR model outperformed (in terms of AUC): 1) individual features (no ensemble) and 2) a random forest classifier ensemble obtained by bagging multiple decision tree classifiers. The apparent drop-off in AUC at higher image resolutions is due to lack of fine detail in the expert annotation of CaP and is not an artifact of the classifier. The implicit feature selection done via the AdaBoost component of the BBMR classifier reveals that different classes and types of image features become more relevant for discriminating between CaP and benign areas at different image resolutions.
Innovation and practice of continuous auditing
(2011)

Article history: Received 27 August 2010 Received in revised form 23 December 2010 Accepted 6 January 2011 The traditional audit paradigm is outdated in the real time economy. Innovation of the traditional audit process is necessary to support real time assurance. Practitioners and academics are exploring continuous auditing as a potential successor to the traditional audit paradigm. Using technology and automation, continuous auditing methodology enhances the efficiency and effectiveness of the audit process to support real time assurance. This paper defines how continuous auditing methodology introduces innovation to practice in seven dimensions and proposes a four-stage paradigm to advance future research. In addition, we formulate a set of methodological propositions concerning the future of assurance for practitioners and academic researchers. © 2011 Elsevier Inc. All rights reserved.
Severity Analyses of Single-Vehicle Crashes Based on Rough Set Theory
(2009)

A single-vehicle crash is a typical pattern of traffic accidents and tends to cause heavy loss. The purpose of this study is to identify the factors significantly influencing single-vehicle crash injury severity, using a data selected from Beijing city for a 4-year period. Rough set theory was applied to complete the injury severity analysis, and followed by applying cross-validation method to estimate the prediction accuracy of extraction rules. Results show that it is effective for analyzing the severity of Single-vehicle crashes with rough set theory.
Galvanometric optical laser beam steering system for microfactory application
(2013)

This article presents a kinematic model and control of a galvanometric laser beam steering system for high precision marking, welding or soldering applications as a microfactory module. Galvo systems are capable of scanning laser beam with relatively high frequencies that makes them suitable for fast processing applications. For the sake of flexibility and ease of use 2D reference shapes to be processed are provided as CAD drawings. Drawings are parsed and interpolated to x - y reference data points on MATLAB then stored as arrays in C code. C header file is further included as reference data points to be used by the system. Theoretical kinematic model of the system is derived and model parameters are tuned for practical implementation and validated with respect to measured positions on rotation space with optical position sensor and image field with position sensitive device. Machining with material removal requires high power laser to be employed that makes position measurement on image field unfeasible. Therefore for closed loop applications optical position sensor embedded in galvo motors is used for position feedback. Since the model approved to be approximately linear in the range of interest by simulations, a PI controller is used for precise positioning of the galvo motors. Experimental results for tracking circular and rectangular shape references are proved to be precise with errors of less than 2%.
Local potassium signaling couples neuronal activity to vasodilation in the brain
(2006)

The mechanisms by which active neurons, via astrocytes, rapidly signal intracerebral arterioles to dilate remain obscure. Here we show that modest elevation of extracellular potassium (K+) activated inward rectifier K+ (Kir) channels and caused membrane potential hyperpolarization in smooth muscle cells (SMCs) of intracerebral arterioles and, in cortical brain slices, induced Kir-dependent vasodilation and suppression of SMC intracellular calcium (Ca2+) oscillations. Neuronal activation induced a rapid (<2 s latency) vasodilation that was greatly reduced by Kir channel blockade and completely abrogated by concurrent cyclooxygenase inhibition. Astrocytic endfeet exhibited large-conductance, Ca2+-sensitive K+ (BK) channel currents that could be activated by neuronal stimulation. Blocking BK channels or ablating the gene encoding these channels prevented neuronally induced vasodilation and suppression of arteriolar SMC Ca2+, without affecting the astrocytic Ca2+ elevation. These results support the concept of intercellular K+ channel–to–K+ channel signaling, through which neuronal activity in the form of an astrocytic Ca2+ signal is decoded by astrocytic BK channels, which locally release K+ into the perivascular space to activate SMC Kir channels and cause vasodilation.
Winnowing: Local Algorithms for Document Fingerprinting
(2003)

Digital content is for copying: quotation, revision, plagiarism, and file sharing all create copies. Document fingerprinting is concerned with accurately identifying copying, including small partial copies, within large sets of documents.We introduce the class of local document fingerprinting algorithms, which seems to capture an essential property of any finger-printing technique guaranteed to detect copies. We prove a novel lower bound on the performance of any local algorithm. We also develop winnowing, an efficient local fingerprinting algorithm, and show that winnowing's performance is within 33% of the lower bound. Finally, we also give experimental results on Web data, and report experience with MOSS, a widely-used plagiarism detection service.
Analysis of Metastability Errors in Conventional, LSB-First, and Asynchronous SAR ADCs
(2016)

A practical model for characterizing comparator metastability errors in SAR ADCs is presented, and is used to analyze not only the conventional SAR but also LSB-first and asynchronous versions. This work makes three main contributions: first, it is shown that for characterizing metastability it is more reasonable to use input signals with normal or Laplace distributions. Previous work used uniformly-distributed signals in the interest of making derivations easier, but this simplifying assumption overestimated SMR by as much as 18 dB compared to the more reasonable analysis presented here. Second, this work shows that LSB-first SAR ADCs achieve SMR performance equal to or better than conventional SARs with the same metastability window, depending on bandwidth. Finally, the analysis is used to develop a framework for calculating the maximum effective sample rate for asynchronous SAR ADCs, and in doing so demonstrates that proximity detectors are not effective solutions to improving metastability performance.
PixelNet: Representation of the pixels, by the pixels, and for the pixels
(2017)

We explore design principles for general pixel-level prediction problems, from low-level edge detection to midlevel surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fullyconvolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computationally efficient, we point out that such approaches are not statistically efficient during learning precisely because spatial redundancy limits the information learned from neighboring pixels. We demonstrate that stratified sampling of pixels allows one to (1) add diversity during batch updates, speeding up learning; (2) explore complex nonlinear predictors, improving accuracy; and (3) efficiently train state-of-the-art models tabula rasa (i.e., “from scratch”) for diverse pixel-labeling tasks. Our single architecture produces state-of-the-art results for semantic segmentation on PASCAL-Context dataset, surface normal estimation on NYUDv2 depth dataset, and edge detection on BSDS.
Culture Wires the Brain: A Cognitive Neuroscience Perspective.
(2010)

There is clear evidence that sustained experiences may affect both brain structure and function. Thus, it is quite reasonable to posit that sustained exposure to a set of cultural experiences and behavioral practices will affect neural structure and function. The burgeoning field of cultural psychology has often demonstrated the subtle differences in the way individuals process information-differences that appear to be a product of cultural experiences. We review evidence that the collectivistic and individualistic biases of East Asian and Western cultures, respectively, affect neural structure and function. We conclude that there is limited evidence that cultural experiences affect brain structure and considerably more evidence that neural function is affected by culture, particularly activations in ventral visual cortex-areas associated with perceptual processing.
Passive Self-Interference Suppression for Full-Duplex Infrastructure Nodes
(2014)

Recent research results have demonstrated the feasibility of full-duplex wireless communication for short-range links. Although the focus of the previous works has been active cancellation of the self-interference signal, a majority of the overall self-interference suppression is often due to passive suppression, i.e., isolation of the transmit and receive antennas. We present a measurement-based study of the capabilities and limitations of three key mechanisms for passive self-interference suppression: directional isolation, absorptive shielding, and cross-polarization. The study demonstrates that more than 70 dB of passive suppression can be achieved in certain environments, but also establishes two results on the limitations of passive suppression: (1) environmental reflections limit the amount of passive suppression that can be achieved, and (2) passive suppression, in general, increases the frequency selectivity of the residual self-interference signal. These results suggest two design implications: (1) deployments of full-duplex infrastructure nodes should minimize near-antenna reflectors, and (2) active cancellation in concatenation with passive suppression should employ higher-order filters or per-subcarrier cancellation.
System-level performance evaluation of downlink non-orthogonal multiple access (NOMA)
(2013)

As a promising downlink multiple access scheme for further LTE enhancement and future radio access (FRA), this paper investigates the system-level performance of non-orthogonal multiple access (NOMA) with a successive interference canceller (SIC) on the receiver side. The goal is to clarify the potential gains of NOMA over orthogonal multiple access (OMA) such as OFDMA, taking into account key link adaptation functionalities of the LTE radio interface such as adaptive modulation and coding (AMC), hybrid automatic repeat request (HARQ), time/frequency-domain scheduling, and outer loop link adaptation (OLLA), in addition to NOMA specific functionalities such as dynamic multi-user power allocation. Based on computer simulations, we show under multiple configurations that the system-level performance achieved by NOMA is superior to that for OMA.
Non-Orthogonal Multiple Access With Cooperative Full-Duplex Relaying
(2016)

We propose a full-duplex (FD) cooperative non-orthogonal multiple access (NOMA) system with dual users, where a dedicated FD relay assists the information transmission to the user with weak channel condition. Under the realistic assumption of imperfect self-interference cancellation, the achievable outage probability of both users and ergodic sum capacity are investigated, and exact analytical expressions are derived. Simulation results demonstrate that the proposed FD cooperative NOMA system attains better performance than the half-duplex cooperative NOMA system in the moderate signal-to-noise ratio regime.
An Analysis of Several Heuristics for the Traveling Salesman Problem
(1977)

Several polynomial time algorithms finding "good," but not necessarily optimal, tours for the traveling salesman problem are considered. We measure the closeness of a tour by the ratio of the obtained tour length to the minimal tour length. For the nearest neighbor method, we show the ratio is bounded above by a logarithmic function of the number of nodes. We also provide a logarithmic lower bound on the worst case. A class of approximation methods we call insertion methods are studied, and these are also shown to have a logarithmic upper bound. For two specific insertion methods, which we call nearest insertion and cheapest insertion, the ratio is shown to have a constant upper bound of 2, and examples are provided that come arbitrarily close to this upper bound. It is also shown that for any n => 8, there are traveling salesman problems with n nodes having tours which cannot be improved by making n/4 edge changes, but for which the ratio is 2(1l/n). Key words, traveling salesman problem, approximation algorithm, k-optimal, minimal spanning tree, triangle inequality
A survey of vertical handover decision algorithms in Fourth Generation heterogeneous wireless networks
(2010)

1389-1286/$ see front matter 2010 Elsevier B.V doi:10.1016/j.comnet.2010.02.006 * Corresponding author. Tel.: +61 3 99053503; fa E-mail address: ASekerci@ieee.org (Y. Ahmet S ek Vertical handover decision (VHD) algorithms are essential components of the architecture of the forthcoming Fourth Generation (4G) heterogeneous wireless networks. These algorithms need to be designed to provide the required Quality of Service (QoS) to a wide range of applications while allowing seamless roaming among a multitude of access network technologies. In this paper, we present a comprehensive survey of the VHD algorithms designed to satisfy these requirements. To offer a systematic comparison, we categorize the algorithms into four groups based on the main handover decision criterion used. Also, to evaluate tradeoffs between their complexity of implementation and efficiency, we discuss three representative VHD algorithms in each group. 2010 Elsevier B.V. All rights reserved.
A Survey of Model Comparison Approaches and Applications
(2013)

This survey paper presents the current state of model comparison as it applies to Model-Driven Engineering. We look specifically at how model matching is accomplished, the application of the approaches, and the types of models that approaches are intended to work with. Our paper also indicates future trends and directions. We find that many of the latest model comparison techniques are geared towards facilitating arbitrary meta models and use similarity-based matching. Thus far, model versioning is the most prevalent application of model comparison. Recently, however, work on comparison for versioning has begun to stagnate, giving way to other applications. Lastly, there is wide variance among the tools in the amount of user effort required to perform model comparison, as some require more effort to facilitate more generality and expressive power.
A topological hierarchy for functions on triangulated surfaces
(2004)

We combine topological and geometric methods to construct a multiresolution representation for a function over a two-dimensional domain. In a preprocessing stage, we create the Morse-Smale complex of the function and progressively simplify its topology by cancelling pairs of critical points. Based on a simple notion of dependency among these cancellations, we construct a hierarchical data structure supporting traversal and reconstruction operations similarly to traditional geometry-based representations. We use this data structure to extract topologically valid approximations that satisfy error bounds provided at runtime.
CampusSense — A smart vehicle parking monitoring and management system using ANPR cameras and android phones
(2017)

Vehicle parking monitoring and management has become a big challenge for educational institutions with increasing enrollments, high percentage of vehicle ownership and decreasing parking supply which in result triggering blockage of vehicle, congestion, wastage of time and money. In university campuses particularly in Kingdom of Saudi Arabia, vehicle parking monitoring and management problem is getting worse and more frustrating due to the fact that majority of students, faculty and staff members own cars and drive through them to the university campuses. These common problems include finding out people (evidence) who are responsible for the damages (hitting, scraping, scratching and dents) to other cars and the blockage of car due to wrong car parking which takes much time to locate the owner of the car. Moreover, locating or forgetting their car park location another difficulty that is often faced by the students, faculty and staff members. The existing cameras located at the parking lots are only for video surveillance and cannot help in such situations as there is a lack of proper vehicle parking monitoring and management system. To cope with above mentioned problems and to ensure a better parking experience by accommodating increasing number of vehicles in a proper convenient manner, we propose a smart vehicle parking monitoring and management system called CampusSense. In CampusSense, Automatic Number Plate Recognition (ANPR) cameras and android based mobile application is developed to efficiently monitor, manage and protect the parking facilities at university campuses. Parking problems around the university campus faced by the students, faculty and staff members are analyzed by conducting a survey.
Angry, disgusted, or afraid? Studies on the malleability of emotion perception.
(2008)

Current theories of emotion perception posit that basic facial expressions signal categorically discrete emotions or affective dimensions of valence and arousal. In both cases, the information is thought to be directly "read out" from the face in a way that is largely immune to context. In contrast, the three studies reported here demonstrated that identical facial configurations convey strikingly different emotions and dimensional values depending on the affective context in which they are embedded. This effect is modulated by the similarity between the target facial expression and the facial expression typically associated with the context. Moreover, by monitoring eye movements, we demonstrated that characteristic fixation patterns previously thought to be determined solely by the facial expression are systematically modulated by emotional context already at very early stages of visual processing, even by the first time the face is fixated. Our results indicate that the perception of basic facial expressions is not context invariant and can be categorically altered by context at early perceptual levels.
Simulator sickness in virtual display gaming: a comparison of stereoscopic and non-stereoscopic situations
(2006)

In this paper we compare simulator sickness symptoms produced by racing game in three different conditions. In the first experiment the participants played the Need for Speed car racing game with an ordinary 17" display and in the second and third experiments they used a head-worn virtual display for the game playing. The difference between experiments 2 and 3 was in the use of stereoscopy, as in the third experiment the car racing game was seen in stereoscopic three-dimensions. Our results indicated that there were no significant differences in sickness symptoms when we compared the ordinary display and the virtual display in non-stereoscopic mode. In stereoscopic condition the eye strain and disorientation symptoms were significantly elevated compared to the ordinary display. We conclude that using a virtual display as an accessory in a mobile device is a viable alternative, because the non-stereoscopic virtual display did not produce significantly more sickness symptoms compared to ordinary game playing.